
A class of Fourier based statistics for irregular spaced spatial data is introduced, examples include, the Whittle likelihood, a parametric estimator of the covariance function based on the $L_{2}$-contrast function and a simple nonparametric estimator of the spatial autocovariance which is a non-negative function. The Fourier based statistic is a quadratic form of a discrete Fourier-type transform of the spatial data. Evaluation of the statistic is computationally tractable, requiring $O(nb^{})$ operations, where $b$ are the number Fourier frequencies used in the definition of the statistic and $n$ is the sample size. The asymptotic sampling properties of the statistic are derived using both increasing domain and fixed domain spatial asymptotics. These results are used to construct a statistic which is asymptotically pivotal.
Inference from spatial processes, Asymptotic distribution theory in statistics, Mathematics - Statistics Theory, Statistics Theory (math.ST), spectral density function, quadratic forms, irregular spaced locations, Time series, auto-correlation, regression, etc. in statistics (GARCH), 62M15, FOS: Mathematics, spatial spectral density function, random fields, stationary spatial process, stationary spatial random fields, Inference from stochastic processes and spectral analysis, 62M30, increasing domain asymptotics, fixed domain asymptotics, Fixed and increasing domain asymptotics
Inference from spatial processes, Asymptotic distribution theory in statistics, Mathematics - Statistics Theory, Statistics Theory (math.ST), spectral density function, quadratic forms, irregular spaced locations, Time series, auto-correlation, regression, etc. in statistics (GARCH), 62M15, FOS: Mathematics, spatial spectral density function, random fields, stationary spatial process, stationary spatial random fields, Inference from stochastic processes and spectral analysis, 62M30, increasing domain asymptotics, fixed domain asymptotics, Fixed and increasing domain asymptotics
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